Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 192

This bibliography lists 247 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1979

Building capacity for evidence-based public health: Reconciling the pulls of practice and the push of research

Timely implementation of principles of evidence-based public health (EBPH) is critical for bridging the gap between discovery of new knowledge and its application. Public health organizations need sufficient capacity (the availability of resources, structures, and workforce to plan, deliver, and evaluate the preventive dose of an evidence-based intervention) to move science to practice. We review principles of EBPH, the importance of capacity building to advance evidence-based approaches, promising approaches for capacity building, and future areas for research and practice. Although there is general agreement among practitioners and scientists on the importance of EBPH, there is less clarity on the definition of evidence, how to find it, and how, when, and where to use it. Capacity for EBPH is needed among both individuals and organizations. Capacity can be strengthened via training, use of tools, technical assistance, assessment and feedback, peer networking, and incentives. Modest investments in EBPH capacity building will foster more effective public health practice

Combined cognitive and vocational interventions after mild to moderate traumatic brain injury: study protocol for a randomized controlled trial

Background A considerable proportion of patients with mild to moderate traumatic brain injury (TBI) experience long-lasting somatic, cognitive, and emotional symptoms that may hamper their capacity to return to work (RTW). Although several studies have described medical, psychological, and work-related factors that predict RTW after TBI, well-controlled intervention studies regarding RTW are scarce. Furthermore, there has traditionally been weak collaboration among health-related rehabilitation services, the labor and welfare sector, and workplaces. Methods/design This study protocol describes an innovative randomized controlled trial in which we will explore the effect of combining manualized cognitive rehabilitation (Compensatory Cognitive Training [CCT]) and supported employment (SE) on RTW and related outcomes for patients with mild to moderate TBI in real-life competitive work settings. The study will be carried out in the southeastern region of Norway and thereby be performed within the Norwegian welfare system. Patients aged 18–60 years with mild to moderate TBI who are employed in a minimum 50% position at the time of injury and sick-listed 50% or more for postconcussive symptoms 2 months postinjury will be included in the study. A comprehensive assessment of neurocognitive function, self-reported symptoms, emotional distress, coping style, and quality of life will be performed at baseline, immediately after CCT (3 months after inclusion), following the end of SE (6 months after inclusion), and 12 months following study inclusion. The primary outcome measures are the proportion of participants who have returned to work at 12-month follow-up and length of time until RTW, in addition to work stability as well as work productivity over the first year following the intervention. Secondary outcomes include changes in self-reported symptoms, emotional and cognitive function, and quality of life. Additionally, a qualitative RTW process evaluation focused on organizational challenges at the workplace will be performed. Discussion The proposed study will combine cognitive and vocational rehabilitation and explore the efficacy of increased cross-sectoral collaboration between specialized health care services and the labor and welfare system. If the intervention proves effective, the project will describe the cost-effectiveness and utility of the program and thereby provide important information for policy makers. In addition, knowledge about the RTW process for persons with TBI and their workplaces will be provided. Trial registration ClinicalTrials.gov, NCT03092713. Registered on 10 March 2017

Nature-Inspired Adaptive Architecture for Soft Sensor Modelling

This paper gives a general overview of the challenges present in the research field of Soft Sensor building and proposes a novel architecture for building of Soft Sensors, which copes with the identified challenges. The architecture is inspired and making use of nature-related techniques for computational intelligence. Another aspect, which is addressed by the proposed architecture, are the identified characteristics of the process industry data. The data recorded in the process industry consist usually of certain amount of missing values or sample exceeding meaningful values of the measurements, called data outliers. Other process industry data properties causing problems for the modelling are the collinearity of the data, drifting data and the different sampling rates of the particular hardware sensors. It is these characteristics which are the source of the need for an adaptive behaviour of Soft Sensors. The architecture reflects this need and provides mechanisms for the adaptation and evolution of the Soft Sensor at different levels. The adaptation capabilities are provided by maintaining a variety of rather simple models. These particular models, called paths in terms of the architecture, can for example focus on different partition of the input data space, or provide different adaptation speeds to changes in the data. The actual modelling techniques involved into the architecture are data-driven computational learning approaches like artificial neural networks, principal component regression, etc

Aerospace Medicine and Biology. A continuing bibliography (Supplement 226)

This bibliography lists 129 reports, articles, and other documents introduced into the NASA scientific and technical information system in November 1981

Integrating Emerging Areas of Nursing Science into PhD Programs

The Council for the Advancement of Nursing Science aims to “facilitate and recognize life-long nursing science career development” as an important part of its mission. In light of fast-paced advances in science and technology that are inspiring new questions and methods of investigation in the health sciences, the Council for the Advancement of Nursing Science convened the Idea Festival for Nursing Science Education and appointed the Idea Festival Advisory Committee to stimulate dialogue about linking PhD education with a renewed vision for preparation of the next generation of nursing scientists. Building on the 2010 American Association of Colleges of Nursing Position Statement “The Research-Focused Doctoral Program in Nursing: Pathways to Excellence,” Idea Festival Advisory Committee members focused on emerging areas of science and technology that impact the ability of research-focused doctoral programs to prepare graduates for competitive and sustained programs of nursing research using scientific advances in emerging areas of science and technology. The purpose of this article is to describe the educational and scientific contexts for the Idea Festival, which will serve as the foundation for recommendations for incorporating emerging areas of science and technology into research-focused doctoral programs in nursing

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 359)

This bibliography lists 164 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Jan. 1992. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

Self-adaptive and sensitivity-aware QoS modeling for the cloud

Given the elasticity, dynamicity and on-demand nature of the cloud, cloud-based applications require dynamic models for Quality of Service (QoS), especially when the sensitivity of QoS tends to fluctuate at runtime. These models can be autonomically used by the cloud-based application to correctly self-adapt its QoS provision. We present a novel dynamic and self-adaptive sensitivity-aware QoS modeling approach, which is fine-grained and grounded on sound machine learning techniques. In particular, we combine symmetric uncertainty with two training techniques: Auto-Regressive Moving Average with eXogenous inputs model (ARMAX) and Artificial Neural Network (ANN) to reach two formulations of the model. We describe a middleware for implementing the approach. We experimentally evaluate the effectiveness of our models using the RUBiS benchmark and the FIFA 1998 workload trends. The results show that our modeling approach is effective and the resulting models produce better accuracy when compared with conventional models